Finite-Difference Time-Domain Simulation for Three-dimensional Polarized Light Imaging

Three-dimensional Polarized Light Imaging (3D-PLI) is a promising technique to reconstruct the nerve fiber architecture of human post-mortem brains from birefringence measurements of histological brain sections with micrometer resolution. To better understand how the reconstructed fiber orientations are related to the underlying fiber structure, numerical simulations are employed. Here, we present two complementary simulation approaches that reproduce the entire 3D-PLI analysis: First, we give a short review on a simulation approach that uses the Jones matrix calculus to model the birefringent myelin sheaths. Afterwards, we introduce a more sophisticated simulation tool: a 3D Maxwell solver based on a Finite-Difference Time-Domain algorithm that simulates the propagation of the electromagnetic light wave through the brain tissue. We demonstrate that the Maxwell solver is a valuable tool to better understand the interaction of polarized light with brain tissue and to enhance the accuracy of the fiber orientations extracted by 3D-PLI.Comment: 13 pages, 5 figure

Designed nanomolar small-molecule inhibitors of Ena/VASP EVH1 interaction impair invasion and extravasation of breast cancer cells

Battling metastasis through inhibition of cell motility is considered a promising approach to support cancer therapies. In this context, Ena/VASP-depending signaling pathways, in particular interactions with their EVH1 domains, are promising targets for pharmaceutical intervention. However, protein-protein interactions involving proline-rich segments are notoriously difficult to address by small molecules. Hence, structure-based design efforts in combination with the chemical synthesis of additional molecular entities are required. Building on a previously developed nonpeptidic micromolar inhibitor, we determined 22 crystal structures of ENAH EVH1 in complex with inhibitors and rationally extended our library of conformationally defined prolinederived modules (ProMs) to succeed in developing a nanomolar inhibitor (K-d = 120 nM, MW = 734 Da). In contrast to the previous inhibitor, the optimized compounds reduced extravasation of invasive breast cancer cells in a zebrafish model. This study represents an example of successful, structure-guided development of low molecular weight inhibitors specifically and selectively addressing a proline-rich sequence-recognizing domain that is characterized by a shallow epitope lacking defined binding pockets. The evolved high-affinity inhibitor may now serve as a tool in validating the basic therapeutic concept, i.e., the sup pression of cancer metastasis by inhibiting a crucial protein- protein interaction involved in actin filament processing and cell migration

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Glioblastomas with primitive neuronal component harbor a distinct methylation and copy‑number profle with inactivation of TP53, PTEN, and RB1

Glioblastoma IDH-wildtype presents with a wide histological spectrum. Some features are so distinctive that they are considered as separate histological variants or patterns for the purpose of classification. However, these usually lack defined (epi-)genetic alterations or profiles correlating with this histology. Here, we describe a molecular subtype with overlap to the unique histological pattern of glioblastoma with primitive neuronal component. Our cohort consists of 63 IDH-wildtype glioblastomas that harbor a characteristic DNA methylation profile. Median age at diagnosis was 59.5 years. Copy-number variations and genetic sequencing revealed frequent alterations in TP53, RB1 and PTEN, with fewer gains of chromosome 7 and homozygous CDKN2A/B deletions than usually described for IDH-wildtype glioblastoma. Gains of chromosome 1 were detected in more than half of the cases. A poorly differentiated phenotype with frequent absence of GFAP expression, high proliferation index and strong staining for p53 and TTF1 often caused misleading histological classification as carcinoma metastasis or primitive neuroectodermal tumor. Clinically, many patients presented with leptomeningeal dissemination and spinal metastasis. Outcome was poor with a median overall survival of only 12 months. Overall, we describe a new molecular subtype of IDH-wildtype glioblastoma with a distinct histological appearance and genetic signature.publishedVersio

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs